Computer implemented system and method for assessing a neuropsychiatric condition of a human subject

ABSTRACT

The disclosure provides methods for assessing a neuropsychiatric condition of a human subject by combining the subject&#39;s biomarker data and thought marker data into a quantitative assessment of the subject&#39;s neuropsychiatric condition.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional App. Ser. No.61/214,707, filed Apr. 27, 2009, the entire disclosure of which isincorporated herein by reference.

BACKGROUND

There is a need for more accurate assessment of human subject'sneuropsychiatric conditions so that the human subject may be bettertreated for such conditions by their caregivers. For example, there areneeds for better assessment of a suicide risk in an individual, thereare needs for better assessment of end-of-life treatment care forterminally ill patients, there are needs for better assessment andtreatment of schizophrenic patients, there are needs for betterassessment and handling of a criminal act (or repeat criminal act)attempt risk for an individual, there are needs for better assessmentand treatment of other neuropsychiatric conditions, and there are needsfor better assessment and handling of those feigning neuropsychiatricconditions.

With respect to suicide risk, it is estimated that each year 800,000people die by suicide worldwide. In the United States alone, eightypeople kill themselves each day, twelve under the age 25. Expertsestimate the total life time costs of suicide to be $33 billion. TheCenters for Disease Control and Prevention, however, notes thatapproximately 15% of all high-school students have developed a seriousplan to attempt suicide, 9% have attempted suicide, and nearly 3% haverequired medical attention due to a suicide attempt. In an average year,a typical pediatric emergency department evaluates at least 2,000patients exhibiting suicidal behavior. A challenge for those who carefor suicide attempters may be assessing the likelihood of anotherserious suicide attempt, which may be lethal.

With respect to end-of-life treatment and care of terminally illsubjects, One option is to support a clinical atmosphere thatunderstands when death is certain, and knows when to shift from lifesaving medical care to preparing for the inevitable, death. In the latercase, establishing expectations and providing specialized end-of-lifecare becomes the norm. With children, especially, understanding thedying child's concerns can be difficult. These children may be anxiousabout pain and discomfort, they may struggle with what will happen tothem when they die, or they may worry about making family members sad,they may feel alone, stupid, or angry. As care providers understand thedying child's concerns they can better provide personalized care to thechild and family.

SUMMARY

A method for assessing a neuropsychiatric condition (such as, but notlimited to, a risk that a subject may attempt to commit suicide orrepeat an attempt to commit suicide, a risk that terminally ill patientis not being care-for or treated according to the patient's true wishes,a risk that a subject may perform or repeat a criminal act and/or aharmful act, a risk of the subject having a psychiatric illness, and/ora risk of a subject feigning a psychiatric illness) may be provided.Such method may be operating from one or more memory devices includingcomputer-readable instructions configured to instruct a computerizedsystem to perform the method, and the method may be operating on acomputerized system including one or more computer servers (or otheravailable devices) accessible over a computer network such as theInternet or over some other data network. The method may include aplurality of steps. A step may include receiving biomarker dataassociated from an analysis of the subject's biological sample and astep of receiving thought-marker data obtained pertaining to one or moreof the subject's recorded thoughts, spoken words, transcribed speech,and writings. A step may include generating a biomarker score associatedwith the neuropsychiatric condition from the biomarker data. A step mayinclude generating a thought-marker score associated with theneuropsychiatric condition from the thought-marker data. And a step mayinvolve calculating a neuropsychiatric condition score based, at leastin part, upon the biomarker score and the thought-marker score. As willbe appreciated, many of these steps do not necessarily need to beperformed in the order provided and some of the steps may be combinedinto a single step or operation.

In an embodiment, the step of generating the biomarker score may includea step of assessing a level of at least a cytokine, a metabolite, apolymorphism, a genotype, a polypeptide, and an mRNA of the humansubject. For example, the step of generating the biomarker score mayinclude a step of assessing a level of a hydroxyindoleaceticacid(5HIAA).

In an embodiment, the step of generating a thought-marker score includesa step of determining a correlation between (a) the human subject'srecorded thoughts, spoken words, transcribed speech and/or writings; and(b) a corpus of thought data collected pertaining, at least in part, tothe neuropsychiatric condition. Further, this correlation may bedetermined, at least in part, utilizing natural language processingand/or machine learning algorithms.

In an embodiment, the method may further include a step of receivingclinical data of the subject associated with the neuropsychiatriccondition; may include a step of generating a clinical data score fromthe clinical data; and the step of calculating in neuropsychiatriccondition score may be based, at least in further part, upon theclinical data score. Further, the clinical data of the subjectassociated with the neuropsychiatric condition may include at least aportion of medical patient record data associated with the subject; mayinclude demographic data associated with the subject; and/or may includeinterview and/or survey data obtained from the subject. With thisembodiment, it is possible that the step of calculating aneuropsychiatric condition score may include steps of (a) normalizingthe biomarker score, (b) normalizing the thought-marker score, (c)normalizing the clinical data score and (d) calculating a mean of atleast the normalized biomarker, thought marker and clinical data scores.Further, the normalizing steps normalize between a numerical scale of0.0 to 1.0 and/or a scale of 0 and 10^(N), wherein N is an integer.Further, the step of generating a clinical data score may include a stepof calculating a percentage of risks associated with theneuropsychiatric condition from the subject compared to a predeterminedset of risks associated with the neuropsychiatric condition.

In an embodiment, the step of generating a biomarker score includes astep of calculating a composite score related to two or more biologicalmarkers associated with the neuropsychiatric condition from thebiomarker data.

In an embodiment, the step of calculating a neuropsychiatric conditionscore includes steps of (a) normalizing the biomarker score, (b)normalizing the thought marker score and (c) calculating a mean of atleast the normalized biomarker and the thought marker scores.

In an embodiment, the method further includes a step of automaticallyrecommending, based upon the calculated neuropsychiatric conditionscore, a subject's treatment regimen, a subject's counseling session, asubject's intervention program and/or a subject's care program.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow diagram depicting an exemplary embodiment of thepresent invention.

FIG. 2 is a flow diagram depicting another exemplary embodiment of thepresent invention.

FIG. 3 is a flow diagram depicting another exemplary embodiment of thepresent invention.

FIG. 4 is a diagram depicting yet another exemplary embodiment of thepresent invention.

FIG. 5 is a diagram depicting another exemplary embodiment of thepresent invention.

FIG. 6. is a flow diagram depicting another exemplary embodiment of thepresent invention.

FIG. 7 is a flow diagram depicting another exemplary embodiment of thepresent invention.

DETAILED DESCRIPTION

In the following detailed description, reference is made to theaccompanying drawings, which form a part hereof. In the drawings,similar symbols typically identify similar components, unless contextdictates otherwise. The illustrative embodiments described in thedetailed description, drawings, and potential points of novelty are notmeant to be limiting. Other embodiments may be utilized, and otherchanges may be made, without departing from the spirit or scope of thesubject matter presented here. It will be readily understood that theaspects of the present disclosure, as generally described herein, andillustrated in the Figures, may be arranged, substituted, combined, anddesigned in a wide variety of different configurations, all of which areexplicitly contemplated and make part of this disclosure.

This disclosure is drawn to methods and systems related to assessingneuropsychiatric conditions of human subjects. A first exampleimplementation involves assessing suicide risks of human subjects. Thisfirst example implementation will be described in detail; and, as willbe appreciated by those of ordinary skill, other example implementationsdescribed and/or contemplated herein (such as, for example, theend-of-life care assessment and the schizophrenia assessment) will bereadily implemented using the methodologies, components and systems ofthis first example embodiment.

I. Suicide Risk Impementation

As depicted in FIG. 1, this embodiment may include an operation 10 ofgathering biological sample(s) from the human subject. This embodimentalso may include an operation 12 of gathering clinical risk factor(s)such as the human subject's suicide attempt history, suicidal intent,psychological health, interpersonal relationships, behavior, familyhistory of suicide, access to weapons, psychosocial stressors and/orother similar clinical risk factors. This embodiment further may includean operation 14 of gathering thought marker(s) related to the humansubject's thoughts, spoken words, transcribed speech, writings and/orother similar thought markers. This embodiment may further include anoperation 16 of analyzing the biological sample to identify suicide riskassociated biological marker(s). These suicide risk associatedbiological marker(s) may relate to cytokine(s), metabolite(s),polymorphism(s), genotype(s), polypeptide(s), mRNA of the human subjectand/or other similar biological marker(s).

This exemplary embodiment may include analyzing the biological marker(s)by using known analysis methods including, without limitation, thosediscussed below. This analysis may result in the determination of abiological marker score. This embodiment may also include acomputer-implemented operation 18 of analyzing the clinical riskfactor(s) by using known analysis methods including, without limitation,those discussed below. This analysis may result in the determination ofa clinical risk factor score. This embodiment may further include acomputer-implemented operation 20 of comparing the thought marker(s) tosuicide notes (such as those developed from a predetermined suicide notedatabase, for example). This comparison may result in the determinationof a thought marker score. This embodiment may further include thecomputer-implemented operation 22 of calculating a suicide risk scorebased, at least in part, on the biological marker score(s), the clinicalrisk factors score(s) and/or the thought marker score(s). Thecalculation of the suicide risk score may be implemented in many ways,including utilizing averages, weighted formulas, normalization ofscores, regressions and/or other similar calculation implementations.This suicide risk score may be provided to the doctor(s), clinician(s),nurse(s), parent(s) or others and may be used in the determination ofwhether further treatment, counseling, observation or intervention isappropriate (e.g., to help prevent a next or even initial suicideattempt by the patient if the suicide risk score is at or above acertain level).

In another exemplary embodiment, the method may include analyzingbiological marker(s) to determine a composite biological marker score.The composite biological marker score may be a score including two ormore biological marker scores, each related to a suicide risk associatedbiological marker. For example, a composite score could be determinedbased on a cytokine biological marker score, a polymorphism biologicalmarker score and a genotype biological marker score. In that example,the composite biological marker score may be determined by acomputerized system in many ways, including utilizing averages, weightedformulas, normalization of scores, regressions and/or other similarcalculation implementations. In yet another embodiment, the compositebiological marker score may be a score including two or more biologicalmarker scores, each biological marker scores related to the same type ofsuicide risk associated biological marker. For example, a compositescore could be determined based on a first metabolite biological markerscore and a second metabolite biological marker score.

Another exemplary embodiment, as depicted in FIG. 2, may include amethod of assessing a suicide risk (initial risk or follow-up risk) of ahuman subject. This embodiment may include an operation 24 of gatheringbiological sample(s) from the human subject. This embodiment further mayinclude the operation 26 of gathering thought marker(s) related to thehuman subject's thoughts, spoken words, transcribed speech, writingsand/or other similar thought markers. In this embodiment, the biologicalsample may include suicide risk associated biological marker(s). Thesesuicide risk associated biological marker(s) may relate to cytokine(s),metabolite(s), polymorphism(s), genotype(s), polypeptide(s), mRNA of thehuman subject and/or other similar biological marker(s).

This exemplary embodiment may include an operation 28 of analyzing thebiological marker(s) by using known analysis methods including, withoutlimitation, those discussed below. This analysis may result in thedetermination of a biological marker score. This embodiment may furtherinclude a computer-implemented operation 30 of comparing the thoughtmarker(s) to suicide notes (such as those developed from a predeterminedsuicide note database, or a corpus of suicide note language, forexample). This comparison may result in the determination of a thoughtmarker score. This embodiment may further include a computer-implementedoperation 32 of calculating a suicide risk score based, at least inpart, on the biological marker score(s) and/or the thought markerscore(s). The calculation of the suicide risk score may be implementedin many ways, including utilizing averages, weighted formulas,normalization of scores, regressions and/or other similar calculationimplementations.

In one embodiment, the method may include a tool utilized by a physicianin evaluating potential treatment regimens for a patient who hasexhibited at least one suicidal attribute such as, but not limited to, asuicide attempt.

In another exemplary embodiment as depicted in FIG. 3, the invention mayinclude a computer-readable medium having instructions configured toperform computer-implemented operations. These operations may include anoperation 34 of receiving biological marker score(s), an operation 36 ofreceiving clinical risk factor(s) and an operation 38 of receivingthought marker(s). The biological marker score(s) may be related tosuicide risk associated biological marker(s) in biological sample(s) ofa human subject. The suicide risk associated biological marker(s) may berelated to cytokine(s), metabolite(s), polymorphism(s), genotype(s),polypeptide(s), mRNA of the human subject and/or other similarbiological marker(s). The clinical risk factor(s) may be related to thehuman subject's suicide attempt history, suicidal intent, psychologicalhealth, interpersonal relationships, behavior, family history ofsuicide, access to weapons, psychosocial stressors and/or other similarclinical risk factors. The thought marker(s) may be related to the humansubject's thoughts, spoken words, transcribed, writings and/or othersimilar thought markers.

The computer readable instructions in this embodiment may also includeinstructions for performing the operation 40 of analyzing the clinicalrisk factor(s) by using known analysis methods including, withoutlimitation, those discussed below. This analysis may result in thedetermination of a clinical risk factor score. The computer readableinstructions in this embodiment may further include instructions forperforming the operation 42 of comparing the thought marker(s) tosuicide notes (such as those found in a predetermined suicide notedatabase, for example). This comparison may result in the determinationof a thought marker score. The computer readable instructions in thisembodiment may further include instructions for performing the operationof calculating a suicide risk score based, at least in part, on thebiological marker score(s), the clinical risk factors score(s) and/orthe thought marker score(s). The calculation of the suicide risk scoremay be implemented in many ways, including utilizing averages, weightedformulas, normalization of scores, regressions and/or other similarcalculation implementations.

FIG. 4 provides an exemplary system for assessing a suicide risk of ahuman subject. In this embodiment, users at one or more locations (forexample, locations 46 a through 46 n, where n corresponds to any number)may communicate with biological marker database(s) 48 and thought markerdatabase(s) 50. This communication may be implemented through anynetwork connection 52 (such as the Internet or an intranet, forexample). Locations may include medical facilities, hospitals, researchfacilities, laboratories, blood testing centers and other similarlocations. Users may transmit or request and receive data to thebiological marker database(s) and thought marker database(s). Thebiological marker database(s) and thought marker database(s) may also bein communication with a suicide risk assessment inference engine 54. Thesuicide risk assessment inference engine may receive data from thebiological marker database(s) 48 and thought marker database(s) 50 andoutput a suicide risk score based, at least in part on the data and oneor more predefined rule sets. The users at the one or more locations mayreceive the suicide risk score from the suicide risk assessmentinference engine 54.

In yet another embodiment, a system for assessing a suicide risk of ahuman subject may be provided. This system may include a computersystem, server system(s) in communication with the computer system and asuicide risk interface (including a graphical user interface) stored onthe server system(s) and accessible by the computer system. The suiciderisk interface may provide suicide risk information related to suiciderisk associated biological marker(s), clinical risk factor(s) andthought marker(s). In one embodiment, the system may generate a suiciderisk score based, at least in part, on the suicide risk associatedbiological marker(s), the clinical risk factor(s) and/or the thoughtmarker(s). In one embodiment, the suicide risk score may be a numericalvalue. In another embodiment, the numerical value may be a numericalvalue that is relative to the numerical value of other human subjects.

In another embodiment, the suicide risk interface may generate a suiciderisk quartile based, at least in part, on suicide risk associatedbiological marker(s), clinical risk factor(s) and thought marker(s),where the suicide risk quartile may be a quartile that is relative tothe quartile of other human subjects.

In yet another embodiment, the suicide risk interface may generate abiological marker score based, at least in part, on suicide riskassociated biological marker(s), a clinical risk factor score based, atleast in part, on the clinical risk factor(s), and a thought markerscore based, at least in part, on thought marker(s).

In another embodiment, the suicide risk interface may normalize thebiological marker score(s), the clinical risk factor score(s) and/or thethought marker score(s). This normalization may generate normalizedbiological marker score(s), normalized clinical risk factor score(s)and/or normalized thought marker score(s). In this embodiment, thesuicide risk interface may generate a suicide risk score based, at leastin part, on the biological marker score(s), the clinical risk factorscore(s), the thought marker score(s), the normalized biological markerscore(s), the normalized clinical risk factor score(s) and thenormalized thought marker score(s).

I.A Biological Analysis

An exemplary embodiment of the present disclosure may provide systemsand methods of assessing a suicide risk in a human subject involvinggathering at least one biological sample and analyzing at least onesuicide risk associated biological marker in the sample to determine abiological marker score.

A “biological sample” may include a sample collected from a subjectincluding, but not limited to, tissues, cells, mucosa, fluid, scrapings,hairs, cell lysates, blood, plasma, serum, and secretions. Biologicalsamples such as blood samples may be obtained by any method known to oneskilled in the art.

A “biological marker” may include any physiological indicator such as,but not limited to, the genotype of the subject at a particular locisuch as a gene, SNP, or portion of a gene, polymorphism, mRNA, cytokine,metabolite, peptide, polypeptide, hormone, neurotransmitter, or celltype. Any means of evaluating a biological marker known in the art maybe utilized in the current methods. A “suicide risk associated”biological marker may include a physiological indicator that has beenlinked to an abnormal frequency of suicide attempts or suicidecompletion. Such an abnormal risk may include an elevated frequency ofsuicide attempts or suicide completion as compared to a healthypopulation or population of subjects at risk for suicide attempts orsuicide completion or a decreased frequency of suicide attempts orsuicide completion within a population of subjects at risk for suicideattempts or suicide completion. Exemplary populations of subjects atrisk for suicide attempts or suicide completion may include, but are notlimited to, subjects who have already made at least one suicide attemptor who have been diagnosed with a disease or condition affiliated withan elevated frequency of suicide attempts or suicide completion, such asschizophrenia.

Suicide risk associated genotypes may include, but are not limited to,suicide risk associated SNPs and allelic variations larger than a singlenucleotide within the coding region of a gene, the exon-intronboundaries, or the 5′ upstream regulatory region of a gene linked to anabnormal frequency of suicide attempts or completions. It iscontemplated that suicide risk associated genotypes may include, but arenot limited to, the S and L alleles of the 5′ upstream regulatory regionof the serotonin transporter gene (5-HTTLPR) (Weizman, 2000 “Serotonintransporter polymorphism and response to SSRIs in major depression andrelevance to anxiety disorders and substance abuse”, Pharmacogenomics,1:335-341; herein incorporated by reference in its entirety).

A suicide risk associated SNP may include, but is not limited to, asingle nucleotide polymorphism (SNP) for which at least one variant hasbeen linked to an abnormal frequency of suicide attempts or completions.It is contemplated that suicide risk associated SNP's may include, butare not limited to, A218C and A779C of the TPH1 gene and A59G of theSLC6A3 gene (Bondy et al (2006) “Genetics of Suicide”, MolecularPsychiatry, 11(4) 336-351 and U.S. 2007/0065821, herein incorporated byreference in their entirety).

Suicide risk associated mRNAs may include, but are not limited to,altered mRNA levels of a gene linked to an abnormal frequency of suicideattempts or completions. It is contemplated that suicide risk associatedmRNAs may include, but are not limited to, the 5-HT(2A) mRNA in theprefrontal cortex and hippocampus (Pandey (2002) “Higher Expression ofserotonin 5-HT(2A) receptors in the postmortem brains of teenage suicidevictims” American J. Psychiatry 159:419-429, herein incorporated byreference in it's entirety). Suicide risk associated polypeptides may bepolypeptides linked to an abnormal frequency of suicide attempts orcompletions.

Suicide risk associated cytokines may include, but are not limited to,cytokines linked to an abnormal frequency of suicide attempts orcompletions. It is contemplated that suicide risk associated cytokinesmay include, but are not limited to, IL-6, IL-2, IFN-γ, IL-4 and TGF-β1.See for example Shaffer et al (1996) “Psychiatric diagnosis in child andadolescent suicide”, Arch Gen Psychiatry 53:339-348 and Kim et al (2007)“Differences in cytokines between non-suicidal patients and suicidalpatients in major depression”, Prog Neuropsychopharmacol BiolPsychiatry, 32:356-61, herein incorporated by reference in theirentirety). Increased IL-6 production may be correlated with decreasedrisk of suicide attempt or completion. Decreased IL-2 may be correlatedwith increased risk of suicide attempt or completion. A shift in theratio of Th1 and Th2 cell types toward the Th1 cell types may beassociated with decreased risk of suicide attempt or completion.

Suicide risk associated neurotransmitters may include, but are notlimited to, neurotransmitters linked to an abnormal frequency of suicideattempts or completions. It is contemplated that suicide risk associatedneurotransmitters may include, but are not limited to, serotonin (5-HT).See for example Pandey (1997) “Protein kinase C in the post mortem brainof teenage suicide victims”, Neurosci Lett 228:111-114 and Samuelsson(2006) “CSF 5-H1AA, suicide intent and hopelessness in the prediction ofearly suicide in male high risk suicide attempters” Acta PsychiatrScanda 113:44-47, herein incorporated by reference in their entirety.

Suicide risk associated metabolites may include, but are not limited to,metabolites either directly linked to an abnormal frequency of suicideattempts or completions or a metabolite of a biological compound linkedto an abnormal frequency of suicide attempts or completions. It iscontemplated that suicide risk associated metabolites may include, butare not limited to, 5-hydroxyindoleaceticacid (5HIAA), a metabolite ofserotonin. Low 5HIAA levels may be linked to elevated risk of suicideattempt or suicide completion. See Nordstrom (1994) “CSF 5-HIAA predictssuicide risk after attempted suicide”, Suicide Life Threat Behav 24:1-9,herein incorporated by reference in its entirety. The number ofdifferent metabolites in humans is unknown but range from approximately2000 to approximately 20,000 compared with significantly higher numbersof proteins and genes (Claudino et al (2007) “Metabolomics: availableresults, current research projects in breast cancer, and futureapplications”, J. Clinical Oncology 25:2840-2846, herein incorporated byreference in its entirety. These small molecule metabolites may be foundin biological samples such as serum or urine. Mass spectroscopy may beused to analyze an individual metabolite or collection of metabolites.See, for example, Wu et al, (2008), “High-throughput tissue extractionprotocol for NMR and MS-based metabolomics”, Analytical Biochemistry372:204-212 and Yee et al (2002) “An NMR Approach to StructuralProteomics”, PNAS 99:1825-30, herein incorporated by reference in theirentirety.

Any method of analyzing a biological marker known in the art may beutilized in the present methods. Methods of analyzing suicide riskassociated biological markers may include, but are not limited to,RT-PCR array profiling such as the Human Th1-Th2-Th3 PCR Array(SABiosciences), DNA microarrays, immunogenic methods, massspectroscopy, HPLC, NMR, DNA sequencing, genotyping, PCR, reversetranscription-PCR, real-time PCR, MALDI-TOF, HPLC, gas chromatographymass spectrometry (GC-MS), liquid chromatography mass spectrometry(LC-MS), Fourier transform mass spectrometry (FT-MS), electronparamagnetic resonance (EPR) spectrometry, atomic force microscopy, andRaman spectroscopy, solid phase ELISA, fluid phase multi-analyteanalysis, fluorescent bead based immunoassay, sandwich basedimmunoassays, and expression analysis (see for example Domon et al(2006) “Mass Spectrometry and Protein Analysis, Science 312:212-217;Walker (2003) Protein Protocols Handbook, 2^(nd) ed, Humana Press; andWalker (2005) Proteomics Protocols Handbook, Humana Press; Winning et al(2007) “Quantitative Analysis of NMR Spectra with Chemometrics”, Journalof Magnetic Resonance 190:26-32; Bowtell & Sambrook (2003) DNAMicroarrays Cold Spring Harbor Laboratory Press; herein incorporated byreference in their entirety.)

Different methods of analyzing suicide risk associated biologicalmarkers may generate different data types. For instance, massspectroscopy may generate a mass/charge ratio while SNP genotyping mayindicate the presence or absence of a particular nucleotide at aspecified residue. Analysis of other biological markers may yield dataabout the concentration of the biomarker, relative concentration data(such as in gene expression analysis), or a detectable v. non-detectableindication. The raw data obtained for each biomarker may be normalizedbefore information about a particular biomarker is incorporated in thebiological marker score.

The following examples are offered by way of illustration and notlimitation.

EXPERIMENTAL Example 1. Biological Sample Collection

Whole blood samples are collected from a human subject using clinicallyacceptable blood collection methods. Two aliquots of 8.5 ml whole bloodare drawn from the subject. One aliquot is centrifuged to separate cellsand sera. Serum samples (200 μl) are utilized in NMR and massspectrometry analysis or cytokine analysis. An additional sample in apurple-top (EDTA containing) tube is utilized in molecular geneticanalysis. (Additional blood samples are obtained and analyzed if thepatient becomes suicidal after the initial evaluation.)

Example 2. Blood Sample Preparation for NMR Studies

Blood samples for Nuclear Magnetic Resonance (NMR) studies are thawed.400 μl saline (0.9% NaCl in 10% D₂O (deuterium oxide) is mixed with theblood sample. The samples are centrifuged at 13400 g for 5 minutes priorto NMR analysis. The blood is prepared according to NMR and clinicalstandards.

Example 3. NMR Data Collection and Analysis

NMR data is collected using a Bruker US2 Avance II NMR spectrometer(Bruker Biospin, Rheinstettin, Germany) operating at 850 MHz ¹Hfrequency and 298K. Data is zero-filled by a factor of two andexponentially weighted by 0.3 Hz of line broadening prior to Fouriertransform, followed by spectral phasing and baseline correction.

Processed spectra are prepared for principal component analysis (PCA)using AMIX. When distinct clustering patterns are observed, models arebuilt for each class. Robust models are selected from these models andare investigated to identify spectral outlier regions correlated withsuicide risk. When significant loadings are identified, chemicalanalysis methods are combined with the spectral analysis.

Example 4. HPLC and HPLC Analysis

Liquid chromatography in the LC-MS system is conducted using an AgilentTechnologies 1200 series HPLC. The LC-MS instrument collects raw data inthe form of individual mass spectra at each time point in the total ionchromatogram. Individual LC-MS analyses are loaded into the sample tablein the Bruker Profile Analysis software package (Bruker Daltonics).

Example 5. DNA Analysis

The Promega Magnesil RED silica-coated magnetic bead kit on a KingFisher96 robotic magnetic bead manipulator is used to extract DNA from a bloodsample.

I.B. Clinical Analysis

An embodiment of the present disclosure may provide systems and methodsof assessing a suicide risk in a human subject involving gatheringclinical risk factor(s) and analyzing the clinical risk factor(s) todetermine a clinical risk factor score.

Clinical characteristics about a human subject may be collected duringpatient interviews, from medical record databases and/or other similarmeans. Medical and/or mental health staff may administer interviews witha subject and/or the subject's parent(s) or guardian(s).

Clinical factors that increase the risk of completed suicide in childrenand adolescents may include (without limitation): high suicidal intentas evidenced by planning, timing and method, a history of previoussuicide attempts, a high level of interpersonal discord, a presence of amood disorder, substance use, a history of impulsive aggression, afamily history of suicidal behavior, access to weapons such as firearms,and recent psychosocial stressors such as conflicts with authority,breakups with significant others or legal issues. Other clinical riskfactors may include evidence of planning, timing the attempt to avoiddetection, not confiding suicidal plans ahead of time and expressing awish to die.

In one or more embodiments, a prior suicide attempt may be a primaryrisk factor for youth suicide, and may greatly elevate the risk of asubsequent suicide completion. The risk for another attempt may be highin the first 3 to 6 months after an unsuccessful suicide attempt, andthe risk may remain elevated for at least several years. Suicidal intentmay be another indicator and risk factor for repetition of suicideattempts and completed suicide.

In an exemplary embodiment, clinical interviews with a subject and/ortheir parent(s) or guardian(s) may include oral and/or writteninterviews. Examples of such interview tools may include (withoutlimitation):

-   -   1. Background form(s) to elicit demographics information    -   2. Suicide history form(s) to elicit exposure to suicide,        connectedness to family, history of neglect or abuse, access to        firearms, sleep habits, etc.    -   3. The Columbia suicide history form(s) to elicit information        about lifetime suicide attempts.    -   4. The Suicide Intent Scale (SIS) to evaluate the severity of        suicidal intent for a previous suicide attempt.    -   5. Family History-Research Diagnostic Criteria (FH-RDC) to        diagnose psychiatric illnesses in first- and second-degree        relatives of subjects.    -   6. Affective Story Task for Speech Sample to measure “Theory of        Mind” ability within the context of emotionally charged        situations. This may be a measure of false-belief understanding        (e.g. one character's beliefs about the mental state of another        character) and consists of positive-, neutral- and        negative-valenced stories. Stories may be matched on word        length, complexity (e.g. details, dialogue, characters and        events) and semantic structure. The positive, neutral and        negative stories may include content consistent with subjective        experience of respectively manic, euthymic or depressed states.        Three stories from each condition may be generated, and each        subject may receive one story from each of the three conditions.        Each story may be read aloud to the interview subject, and the        order of conditions may be counterbalanced across subjects to        control for order effects. Stories may be gender specific;        female subjects may receive a female story version and male        subjects may receive a male story version. Subjects may be        assessed on their ability to recognize that a misleading series        of events could lead one of the characters in the story to        develop a false belief about another character's mental state.        At the end of each story, the subject may be asked a        false-belief question that assesses whether the subject        recognized the potential for misunderstanding. Subject responses        may be recorded and transcribed into a secure database. The        choice of transcribed speech is pragmatic. That is, in an        emergency situation it may be unreasonable to ask the suicidal        patient to write. It may be, however, practical to ask questions        of those patients who are conscious and to receive answers.        These interviews may be retained for subsequent analysis.

In an exemplary embodiment, clinical interviews with just the subjectmay include oral and/or written interviews. Examples of such interviewtools may include (without limitation):

-   -   1. Suicide Probability Scale (SPS): a tool for rating “normals,”        a psychiatric inpatient group, and a suicide attempter group.    -   2. Youth Risk Behavior Survey: a tool related to personal        safety, suicide attempt, tobacco use, alcohol and drug use,        sexual activity, etc.    -   3. Stressful Life Events Schedule (SLES): a tool yielding        information on the occurrence, the date of occurrence, the        duration, and the perceived threat of events experienced by the        patient.    -   4. Achenbach Youth Self-Report/11-18 (YSR): A tool for 5th grade        reading skills that obtains reports from parents, relatives        and/or guardians about children's competencies and        behavioral/emotional problems.    -   5. Affective Story Task for Speech Sample (as discussed        previously).

In an exemplary embodiment, clinical interviews with just the parent(s)and/or guardian(s) may include oral and/or written interviews. Examplesof such interview tools may include (without limitation):

1. Achenbach Child Behavior Checklist for Ages 6-18 (CBCL/6-18).

2. Stressful Life Events Schedule (SLES) (as described previously)

3. Conflict behavior questionnaire form(s)

I.C. Thought Analysis

An embodiment of the present disclosure may provide methods of assessinga suicide risk in a human subject involving gathering thought marker(s)and comparing the thought marker(s) to a plurality of suicide notes todetermine a thought marker score. Natural language processing methodsmay be conducted to determine a correlation between the thoughtmarker(s) and a suicide notes database. U.S. patent application Ser. No.12/006,813, entitled, Processing Text with Domain-Specific SpreadingActivation Methods, by Pestian et. al., provides examples of certainnatural language processing methods that may be used with presentembodiments.

Suicide notes may essentially be artifacts of suicidal thought. It iscontemplated that machine-learning methods can successfullydifferentiate a suicide note (or suicide note wording) from anon-suicide writing (or non-suicide note wording). Such machine-learningmethods may be implemented as software instructions. Suchmachine-learning methods may include linguistic analysis (including opensource algorithms available in “Perl” language, for example). Thislinguistic analysis may include spell checking, tokenizing, filtering,stemming, outlier removal and normalization. Testing of exemplarymachine-learning methods proved to be about 78% accurate at identifyinga suicide note.

Additional analyses may include: mean number of words per sentence,proportion of ambiguous words, percent similarity (the proportion ofwords that were shared between two different corpora—a suicide notedatabase and WordNet, an English language lexical database, forexample), relative entropy (amount of information contained in onecorpus [suicidal patients, for example] compared to another corpus[control patients]), and Squared Chi-square distance. Other knownanalyses may also be implemented.

A number of machine-learning methods may be applied to the transcribeddata to test for differences between suicide notes and non-suicide notewritings. One tool for this analysis may be the Waikato Environment forKnowledge Analysis (Weka). Specificity, sensitivity and F1 may becomputed. Methods useful in this research may be organized into fivecategories.

Decision trees may include: J48/C4.5, Logistic Model Trees,DecisionStump and M5P.

Classification Rules may include: JRIP, Repeated Incremental Pruning toProduce Error Reduction (RIPPER), M5Rules, OneR, and PART.

Function models may include: Sequential minimal optimization,PolyKernel, Puk, RBFKernel, Logistic, and Linear Regression.

Lazy Learners or Instance-based learner methods may include: IBk andLBR.

Meta learner methods may include: AdaBoostM1, Bagging, LogitBoost,MultiBoostAB and Stacking.

Exemplary thought analysis and machine learning methods may include oneor more of the following components: feature selection, expertclassification, word mending, annotation and machine learning.

Feature Selection.

Feature selection, also called variable selection is a data reductiontechnique for selecting the most relevant features for a learningmodels. As irrelevant and redundant features are removed the model'saccuracy increases. Multiple methods for feature selection may be used:bag-of-words, latent semantic analysis and heterogeneous selection. Inone example, heterogeneous selection may be used. To reduceco-linearity, highly correlated features may be removed; to increase thecertainty that a feature is not randomly selected, that feature may berequired to appear in at least 10% of the documents.

Parts of Speech.

A first step may be to tokenize each sentence to determine if additionalanalysis is feasible. This may be done, for example, using a custom Perlprogram. Next, using the Penn-Treebak tag set and/or using TheLingua-EN-Tagger-0.13, 2004 module, for example, several part of speechtags may be added to the feature space. This tagging may be beneficialto establish the relationship of a particular word to a particularconcept.

Readability.

The Flesch and Kincaid readability scores may produce a high informationgain and may be included in the feature space. These scores are designedto indicate comprehension difficulty. They include an ease of readingand text-grade level calculation. Computation of the Flesch and Kincaidindexes may be completed by adding the Lingua::EN::Fathom module to theexemplary Perl program.

Suicidal Emotions.

Collected suicide notes may be annotated with emotional concepts.Developing an ontology to organize these concepts may utilize both thePubmed queries and expert literature reviews. Using the Pubmed queries,a frequency analysis of the key-words in a collection (e.g. 2,000) ofsuicide related manuscripts may be conducted. Expert review of thosekeywords may yield a subset of suicide related manuscripts that containsuicide emotional concepts. These emotional concepts may be allocated toa plurality of different classes. Several mental health professional maythen review each of the collected suicide notes, and assign theemotional concepts found in those notes to the appropriate classes. Forexample, the emotional concepts of guilt may be assigned to the class ofemotional states.

Machine Learning.

There are multiple general types of machine learning: unsupervised,semi-supervised and supervised. Semi-supervised methods use both labeledand unlabeled data and is efficient when labeling data is expensive,which leads to small data sets. In an example approach, thesemi-supervised approach may be selected mainly because the labeled datamay be small. Additionally, exemplary machine learning algorithms forthat may be used, without limitation, may be organized into fivecategories: Decision trees: J48/C4.5, Logistic Model Trees, DecisionStump and M5P; Classification Rules: JRIP, Repeated incremental Pruningto Produce Error Reduction (RIPPER), M5Rules, OneR, and PART; Functionmodels: Sequential minimal optimization (SMO, a variant of SVM),PolyKernel, Puk, RBF Kernel, Logistic, and Linear Regression; LazyLearners or Instance-based learner: Ibo and LBR; Meta learners;AdaBoostM1, Bagging, Logit Boost, Multi Boost AB and Stacking.

Machine Categorization.

The following algorithms may be used to extract and quantify relevantcontent features and create a heterogeneous, multidimensional featurespace:

1. Structure: number of paragraphs,

2. Spelling: number of misspellings (perl module Text::SpellChecker).

3. Tagging: number of tokens, number of words, number of non-wordcharacters, number of sentences, mean frequency of a word, standarddeviation of frequency of a word, maximal frequency of a word, meanlength of a sentence, standard deviation of length of a sentence,maximal length of a sentence, frequency of 32 parts of speech (perlmodule Lingua::EN::Tagger).

4. Readability: Flesch-Kincaid grade level, Flesch reading ease (perlmodule Lingua::EN::Fathom).

5. Parsing: mean depth of a sentence, standard deviation of depth of asentence, maximal depth of a sentence (perl moduleLingua::CollinsParser.

Features arrived from different sources; and so, their numeric valuesnaturally fall in different ranges. For certain machine categorizationalgorithms that means that some features would become more importantthan others. To remedy this problem, feature values were normalizedbased on a maximum value of one. this created a matrix with 66 documentsand 49 features and values between 0 and 1. Since there are fewerfeatures than documents, features selection was not applied.

Algorithm Classification

Decision trees.

Classifier may be represented as a tree. Every node of a tree may berepresented by a decision list. The decision about which branch to go tonext may be based on a single feature response. Leaves of the tree maybe represented by the decisions about which class should be assigned toa single document. The following algorithms may be used:

-   -   J48generates un-pruned or pruned C4.5 revision for 8 decision        trees.    -   LMTimplements ‘Logistic Model Trees.    -   Decision Stump implements decision stumps (trees with a single        split only, i.e. one-level-decision trees), which are frequently        used as base learners for meta learners such as Boosting.

Classification Rules.

Classifier may be represented by a set of logical implications. If acondition for a document is true, then a class is assigned. Conditionmay be composed of a set of feature responses OR-ed or AND-ed together.These rules can also be viewed as a simplified representation of adecision tree. The following algorithms may be used:

-   -   JR implements a fast propositional rules learner, “Repeated        Incremental Pruning to Produce Error Reduction” (RIPPER).    -   OneR builds a simple 1R classifier; it is a set of rules that        test a response of only one attribute.    -   PART generates a set of simplified rules from a C4.5 decision        tree.

Function Models.

Classifiers can be written down as mathematical equations. Decisiontrees and rules typically cannot. There are 2 example classifiers inthis category. The following algorithms may be used:

-   -   SMOI implements a sequential minimal optimization algorithm for        training a support vector classifier using linear kernel.    -   Logistic builds multinomial logistic regression models based on        ridge estimation.    -   Lazy learners. Classifiers in this category may not work until        classification time.

Instance-Based Learning.

May be done by reviewing every instance in the training set separately.An example algorithm that may be used in this category:

-   -   Ibo′ provides a k-nearest neighbors classifier, which uses        Euclidean metric as a distance measure.

Bayesian classifiers.

Classifiers use Bays theorem and the assumption of independence offeatures. An example algorithm that may be used in this category:

-   -   NB implements the probabilistic Naive Bayes classifier.

I.D. Suicide Risk Score Analysis

An embodiment of the present disclosure may provide methods of assessinga suicide risk in a human subject involving calculating a suicide riskscore based, at least in part, on biological marker score(s), clinicalrisk factors score(s) and thought marker score(s). Such calculation maybe implemented in a variety of implementations. In one embodiment, asingle suicide risk score may be calculated. Such a score may assistphysicians and/or medical employee in determining how likely a subjectis to attempt suicide upon or after being released from a medicalfacility.

In an example embodiment, factors of the suicide risk score calculationsmay include biological marker(s), clinical risk factor(s) and thoughtmarker(s). The example table below identifies factors, measurementtool(s), method(s) and example result ranges for each factor.

Example Result Factor Measurement tool(s) Method(s) Range BiologicalMass-spectrometry of Mass/charge ratio  3.0-13.0 Marker(s) 5-HIAA,cytokines, genomic analysis Thought Comparison of Machine-learning0.0-1.0 Marker(s) subject’s thoughts methods and to suicide notecorrelation. database Clinical Subject and Percentage of risks 0.0-1.0Risk parent/guardian present in patients as Factor(s) interviewscompared to all risks.

Analyses may include independent factor analysis of biologicalmarker(s), clinical risk factor(s) and thought marker(s), as discussedpreviously.

Data for one or more factors may be normalized between zero and one tocreate a composite score of the biological marker(s), clinical riskfactor(s) and thought marker(s) and their interaction.

In one embodiment (as shown in the table above), biological markers maybe reported as a scale from 3-13, where a lower concentration may be ofmore concern. On the other hand, regarding the clinical risk factor(s)and thought marker(s), a higher concentration may be of more concern. Inthis case, the biological marker(s) may be normalized using1−(Patient_(i)−min(Range_(i))/max(Range_(i))−min(Range_(i))). In thiscase, the clinical risk factor(s) and thought marker(s) may benormalized using −(P_(i)−min(Range_(i))/max(Range_(i))−min(Range_(i)).An example of such normalizations is shown in the table below. In thistable, a suicide risk score two patients is calculated as a mean of thebiological marker score, clinical risk factor score and thought markerscore.

In another embodiment, the subject's quartile rank as compared to adatabase of other subjects may be calculated. Quartiles (as shown in thetable below) may provide some decision support without purportingexactness.

Clinical Suicide SRS x Biological Thought Risk Risk 10 for Marker MarkerFactor Score ease of Quartile Score Score Score (SRS) reading PositionPatient 1 3 0.78 0.6 Normalized 1.00 0.78 0.6 0.79 8 1st Patient 2 90.45 0.6 Normalized 0.40 0.45 0.6 0.48 5 3rd

To provide additional context for various aspects of the presentinvention, the following discussion is intended to provide a brief,general description of a suitable computing environment in which thevarious aspects of the invention may be implemented. One exemplarycomputing environment is depicted in FIG. 4. While one embodiment of theinvention relates to the general context of computer-executableinstructions that may run on one or more computers, those skilled in theart will recognize that the invention also may be implemented incombination with other program modules and/or as a combination ofhardware and software.

Generally, program modules include routines, programs, components, datastructures, etc., that perform particular tasks or implement particularabstract data types. Moreover, those skilled in the art will appreciatethat aspects of the inventive methods may be practiced with othercomputer system configurations, including single-processor ormultiprocessor computer systems, minicomputers, mainframe computers, aswell as personal computers, hand-held wireless computing devices,microprocessor-based or programmable consumer electronics, and the like,each of which can be operatively coupled to one or more associateddevices. Aspects of the invention may also be practiced in distributedcomputing environments where certain tasks are performed by remoteprocessing devices that are linked through a communications network. Ina distributed computing environment, program modules may be located inboth local and remote memory storage devices.

A computer may include a variety of computer readable media. Computerreadable media may be any available media that can be accessed by thecomputer and includes both volatile and nonvolatile media, removable andnon-removable media. By way of example, and not limitation, computerreadable media may comprise computer storage media and communicationmedia. Computer storage media includes volatile and nonvolatile,removable and non-removable media implemented in any method ortechnology for storage of information such as computer readableinstructions, data structures, program modules or other data. Computerstorage media includes, but is not limited to, RAM, ROM, EEPROM, flashmemory or other memory technology, CD ROM, digital video disk (DVD) orother optical disk storage, magnetic cassettes, magnetic tape, magneticdisk storage or other magnetic storage devices, or any other mediumwhich may be used to store the desired information and which may beaccessed by the computer.

An exemplary environment for implementing various aspects of theinvention may include a computer that includes a processing unit, asystem memory and a system bus. The system bus couples system componentsincluding, but not limited to, the system memory to the processing unit.The processing unit may be any of various commercially availableprocessors. Dual microprocessors and other multi processor architecturesmay also be employed as the processing unit.

The system bus may be any of several types of bus structure that mayfurther interconnect to a memory bus (with or without a memorycontroller), a peripheral bus, and a local bus using any of a variety ofcommercially available bus architectures. The system memory may includeread only memory (ROM) and/or random access memory (RAM). A basicinput/output system (BIOS) is stored in a non-volatile memory such asROM, EPROM, EEPROM, which BIOS contains the basic routines that help totransfer information between elements within the computer, such asduring start-up. The RAM may also include a high-speed RAM such asstatic RAM for caching data.

The computer may further include an internal hard disk drive (HDD)(e.g., EIDE, SATA), which internal hard disk drive may also beconfigured for external use in a suitable chassis, a magnetic floppydisk drive (FDD), (e.g., to read from or write to a removable diskette)and an optical disk drive, (e.g., reading a CD-ROM disk or, to read fromor write to other high capacity optical media such as the DVD). The harddisk drive, magnetic disk drive and optical disk drive may be connectedto the system bus by a hard disk drive interface, a magnetic disk driveinterface and an optical drive interface, respectively. The interfacefor external drive implementations includes at least one or both ofUniversal Serial Bus (USB) and IEEE 1394 interface technologies.

The drives and their associated computer-readable media providenonvolatile storage of data, data structures, computer-executableinstructions, and so forth. For the computer, the drives and mediaaccommodate the storage of any data in a suitable digital format.Although the description of computer-readable media above refers to aHDD, a removable magnetic diskette, and a removable optical media suchas a CD or DVD, it should be appreciated by those skilled in the artthat other types of media which are readable by a computer, such as zipdrives, magnetic cassettes, flash memory cards, cartridges, and thelike, may also be used in the exemplary operating environment, andfurther, that any such media may contain computer-executableinstructions for performing the methods of the invention.

A number of program modules may be stored in the drives and RAM,including an operating system, one or more application programs, otherprogram modules and program data. All or portions of the operatingsystem, applications, modules, and/or data may also be cached in theRAM. It is appreciated that the invention may be implemented withvarious commercially available operating systems or combinations ofoperating systems.

A user may enter commands and information into the computer through oneor more wired/wireless input devices, for example, a keyboard and apointing device, such as a mouse. Other input devices may include amicrophone, an IR remote control, a joystick, a game pad, a stylus pen,touch screen, or the like. These and other input devices are oftenconnected to the processing unit through an input device interface thatis coupled to the system bus, but may be connected by other interfaces,such as a parallel port, an IEEE 1394 serial port, a game port, a USBport, an IR interface, etc.

A display monitor or other type of display device may also be connectedto the system bus via an interface, such as a video adapter. In additionto the monitor, a computer may include other peripheral output devices,such as speakers, printers, etc.

The computer may operate in a networked environment using logicalconnections via wired and/or wireless communications to one or moreremote computers. The remote computer(s) may be a workstation, a servercomputer, a router, a personal computer, a portable computer, a personaldigital assistant, a cellular device, a microprocessor-basedentertainment appliance, a peer device or other common network node, andmay include many or all of the elements described relative to thecomputer. The logical connections depicted include wired/wirelessconnectivity to a local area network (LAN) and/or larger networks, forexample, a wide area network (WAN). Such LAN and WAN networkingenvironments are commonplace in offices, and companies, and facilitateenterprise-wide computer networks, such as intranets, all of which mayconnect to a global communications network such as the Internet.

The computer may be operable to communicate with any wireless devices orentities operatively disposed in wireless communication, e.g., aprinter, scanner, desktop and/or portable computer, portable dataassistant, communications satellite, any piece of equipment or locationassociated with a wirelessly detectable tag (e.g., a kiosk, news stand,restroom), and telephone. This includes at least Wi-Fi (such as IEEE802.11x (a, b, g, n, etc.)) and Bluetooth™ wireless technologies. Thus,the communication may be a predefined structure as with a conventionalnetwork or simply an ad hoc communication between at least two devices.

The system may also include one or more server(s). The server(s) mayalso be hardware and/or software (e.g., threads, processes, computingdevices). The servers may house threads to perform transformations byemploying aspects of the invention, for example. One possiblecommunication between a client and a server may be in the form of a datapacket adapted to be transmitted between two or more computer processes.The data packet may include a cookie and/or associated contextualinformation, for example. The system may include a communicationframework (e.g., a global communication network such as the Internet)that may be employed to facilitate communications between the client(s)and the server(s).

In one exemplary embodiment, as depicted in FIG. 5, medical facilitycomputer system(s) 146 a, 146 b . . . 146 n, may be in communicationwith server system(s) 56. This communication may be implemented throughany network connection 52 (such as the Internet or an intranet, forexample). The server system(s) may include software instructions,database(s), and inference engine and/or a front end (such as agraphical user interface, for example). In the embodiment shown in FIG.5, the software instructions stored on the server(s) may be configuredto implement a suicide risk interface. The server(s) may also beconfigured to provide a suicide risk assessment front end 58. This frontend may be provide to the medical facility computers (146 a-n) userinterface, a graphical user interface or other similar front end. Theserver(s) may also store one or more databases that may includemanagement databases 60, biological marker databases 48, clinical riskfactor databases 62, thought marker databases 50, suicide risk scoredatabases 64 and/or suicide note language databases 66. The managementdatabase(s) 60 may be configured to store and make accessible data usedby the suicide risk interface software instructions, among othercomponents. The biological marker database(s) 48 may be configured tostore and make accessible data associated with biological markers. Theclinical risk factor database(s) 62 may be configured to store and makeaccessible data associated with clinical risk factors. The thoughtmarker database(s) 50 may be configured to store and make accessibledata associated with thought markers. The suicide risk score database(s)64 may be configured to store and make accessible data associated withbiological marker scores, clinical risk factor scores, thought markerscores, suicide risk scores, quartiles and other similar data. Thesuicide note language database(s) 66 may be configured to store and makeaccessible data associated with suicide note language, such as providinga corpus of suicide note language.

II. End-of-Life Assessment and Care Implementation

Beyond the long-standing traditional method of regular conversation withthe terminally ill patient, the present example implementation providesthat at least two additional sources of information may aide thecaregiver in understanding the needs of the dying child and theirfamily. They are thought-markers and biomarkers. Thought-markers can bedescribed as artifacts of thought that are expressed throughconversations and writings. First order thought-markers may includewritings and transcribed conversations of the individual. Second orderthought-markers may include items like facial expressions or the naturalpauses during conversation.

A second source of information are biomarkers that potentially change asdeath approaches. Some biomarkers that are related to tracking deathinclude C-reactive protein (Erlinger, T. P., et al., “C-reactive proteinand the risk of incident colorectal cancer,” JAMA, 2004. 291(5): pp.585-90; and Clarke, R., et al., “Biomarkers of inflammation predict bothvascular and non-vascular mortality in older men,” Eur Heart J, 2008.29(6): pp. 800-9), NGal (Mishra, J., et al., “Neutrophilgelatinase-associated lipocalin (NGAL) as a biomarker for acute renalinjury after cardiac surgery,” Lancet, 2005. 365(9466): pp. 1231-8),cystatin C (Gronroos, M. H., et al., “Comparison of glomerular functiontests in children with cancer; and Shlipak, M. G., et al., “Cystatin Cand the risk of death and cardiovascular events among elderly persons,”N Engl J Med, 2005. 352(20): pp. 2049-60), albumin (Wang, T. J., et al.,“Multiple biomarkers for the prediction of first major cardiovascularevents and death,” N Engl J Med, 2006. 355(25): pp. 2631-9), and variouscytokines (Maletic, V. et al., “Neurobiology of depression: anintegrated view of key findings,” Int J Clin Pract, 2007. 61(12): pp.2030-2040). The references listed above are herein incorporated byreference in their entirety. This example implementation may provideend-of-life care that can be personalized and dispensed based upon theanalyses provided herein.

The terminally ill patient follows a certain illness trajectory whenmoving from health to ill health. This includes three stages: having apotentially curable illness, undergoing intensive treatment, and beingdiagnosed where no curative treatment exists. Often a patient isconsidered to be terminally ill when the life expectancy is estimated tobe six months or less, under the assumption the disease will run itscourse. At each of these stages there is an age-dependent cognitivetrajectory that is hypothesized as tractable. This trajectory mayinclude depression, hopelessness, suicidal ideation, fear, anxiety, andanger. Treating these patients may fall into one of two approaches:palliative and hospice care.

The stress of having a terminal illness can lead to psychiatricdisorders and need for mental health services. In one study, two-hundredand fifty-one pediatric patients with advanced cancer were studied formental illness. Twelve percent met criteria of having Major DepressiveDisorder, Generalized Anxiety Disorder, Panic Disorder, orPost-Traumatic Stress Disorder. Twenty-eight percent had access tomental health services, 17% used those services, and 90% responded thatthey would use mental health services if available (Kadan-Lottick, N.S., et al., “Psychiatric disorders and mental health service use inpatients with advanced cancer: a report from the coping with cancerstudy,” Cancer 2005. 104(12): pp. 2872-81, herein incorporated byreference in its entirety).

One area that appears to have no consideration to date is theapplication of computational linguistics to understand what terminalpatients and parents (family members) are saying as death approaches andhow this differs from the non care patients. This analysis relies oninformation extraction and natural language processing.

II.A. Information Extraction—Thought Markers

The goal of information extraction systems is to extract facts relatedto a particular domain from natural language texts. Texts that areinherently ambiguous, because of hyperbole or metaphors, often cause theaccuracy of an information exatraction system to decline. InformationExtraction extracts data that are either nomothetic or idiographic.Nomothetic data represents statistical-type data, like age, gender,cholesterol levels, and so forth. Extracting information like thefrequency of a rash occurring when a child is prescribed carbamazepinefor epilepsy is a straightforward task as long as the nomothetic dataare available. Ideographic data describe an individual's subjectivecharacteristics like emotions, feelings, and so forth. Extractinginformation like the frequency of rash occurrences by an epilepticadolescent on carbamazepine is, on the other hand, not as straightforward.

Since the early 2000s there has been increased attention focused onideographic information extraction. This focus has concentrated ontopics like polarity (positive or negative) (Turney, P. and M. Littman,“Measuring praise and criticism: Inference of semantic orientation fromassociation,” ACM Transactions on Information Systems-TOIS, 2003. 21(4):pp. 315-346; and Dave, K. S. Lawrence, and D. Pennock, “Mining thepeanut gallery: Opinion extraction and semantic classification ofproduct reviews,” 2003: ACM New York, N.Y., USA), hostility (Spertus,E., “Smokey: Automatic recognition of hostile messages. 1997: JOHN WILEY& SONS LTD.), multi-document summarization (Yu, H. and V.Hatzivassiloglou, “Towards answering opinion questions: Separating factsfrom opinions and identifying the polarity of opinion sentences,” 2003),and tracking sentiments toward events (Tong. R., “An operational systemfor detecting and tracking opinions in on-line discussions. 2001; andSuh, E., E. Diener, and F. Fujita, “Events and subjective well-being:Only recent events matter,” Journal of personality and socialpsychology, 1996, 70(5): pp. 1091-1102) and subsequently there have beenhundreds of papers published on the subject (see above and Das, S. andM. Chen, “Yahoo! for Amazon: Sentiment extraction from small talk on theweb,” Management Science, 2007. 53(9): pp. 1375-1388). The referenceslisted above are incorporated by reference in their entirety. Factorsbehind this interest include: the rise of machine learning methods innatural language processing and information retrieval; the availabilityof datasets for machine learning algorithms to be trained on, due to theblossoming of the World Wide Web and, specifically, the development ofreview-aggregation web-sites; and, of course realization of thefascinating intellectual challenges and commercial and intelligenceapplications. The present example implementation focuses on trackingsentiments about a major life event, in this case death.

II.B. BioMarkers

This example implementation may monitor, in an embodiment, a number ofchemical based biomarkers. Each one has been shown to potentially changeas death approaches. As discussed above, some biomarkers that may berelated to tracking death are: C-reactive protein, NGal, cystatin C,albumin, and various cytokines.

A wide range of biomarkers, reflecting activity in a number ofbiological systems (e.g. neuroendocrine, immune, cardiovascular, andmetabolic), have been found to prospectively predict disability,morbidity, and mortality in older adult populations. For example,Clarke, et al identified a correlation between biomarkers ofinflammation (C-reactive protein, fibrinogen and total/HDL-C) andvascular and non-vascular mortality in older men. Shlipak, et al. showedthat higher cystatin C levels were directly associated, in adose-response manner, with a higher risk of death from all causes.Gruenewald, et al studied 13 different biomarkers in the elderly over a12 year period (n=1189). Using recursive partitioning methods they foundthat most all were associated with high-risk pathways and combinationsof biomarkers were associated with mortality. Wang, et al measured 10biomarkers in 3209 patients attending routine examination cycle of theFramingham Heart study for the prediction of the first majorcardiovascular events and death. They found that using the 10contemporary biomarkers adds only moderately to standard risk factors.Finally, Zethelius, et al. studied the incremental usefulness of addingmultiple biomarkers from different disease pathways for predicting therisk of cardiovascular death. They found that the simultaneous additionof several biomarkers improves the risk stratification for death fromcardiovascular causes. Additionally, some of these cytokines have beenlinked to major mood disorders and suicidal and non-suicidal tendencies(TNF a, IL-6, and IL-10).

The present example implementation provides a system and method thatintegrates biomarkers and thought-marker analysis to result in a betterunderstanding how to increase the quality of care for terminally illpatients.

FIG. 6 provides a flow diagram representing an embodiment of a methodaccording to the present exemplary implementation. This method may beoperating from one or more memory devices including computer-readableinstructions configured to instruct a computerized system to perform themethod, and the method may be operating on a computerized systemincluding one or more computer servers (or other available devices)accessible over a computer network such as the Internet or over someother data network. The method may include the following operations,which do not necessarily need to be performed in the stated order.Operation 70 involves receiving biomarker data obtained from an analysisof a subject's biological sample. Operation 72 involves receivingthought-marker data obtained pertaining to one or more of the subject'srecorded thoughts, spoken words, transcribed speech, writings and/orfacial expressions. Operation 74 involves generating a biomarker scoreassociated with end-of-life treatment relevance from the biomarker data.Operation 76 involves generating a thought-marker score associated withend-of-life treatment relevance from the thought-marker data. Operation78 involves calculating an end-of-life treatment score based, at leastin part, upon the biomarker score and the thought-marker score.

In an embodiment, the step of generating the biomarker score may includea step of accessing a level of one or more chemical based biomarkersfrom the biological sample that have been shown to change as the subjectnears death. Alternatively, or in addition, the step of generating thebiomarker score includes a step of assessing a level of C-reactiveprotein, NGal, cystatin, albumin, IL-6 cytokine, IL-2 cytokine, IFN-γcytokine, IL-4 cytokine and/or TGF-β1 cytokine biomarkers from thebiological sample.

In an embodiment, the step of generating a thought-marker score includesa step of determining a correlation between (a) the one or more of thehuman subject's recorded thoughts, spoken words, transcribed speech,writings and facial expressions; and (b) a corpus of thought datacollected pertaining, at least in part, to the end-of-life treatmentrelevance.

In an embodiment, the step of generating a biomarker score includes astep of calculating a composite score related to two or more biologicalmarkers associated with the end-of-life treatment relevance from thebiomarker data.

In an embodiment, the step of calculating the end-of-life treatmentscore includes steps of: (a) normalizing the biomarker score, (b)normalizing the thought-marker scores, and (c) calculating a mean of atleast the normalized biomarker score and the thought-marker scores.Furthermore, these normalizing steps may normalize between a scale of0.0 and 1.0 and/or a scale of 0 and 10″ where N is an integer (e.g.between 0 and 10, between 0 and 100, between 0 and 1,000 and so forth).

III. Assessment of Neuropsychiatric Conditions

Based upon the above, it will be readily apparent that manyneuropsychiatric conditions may be readily assessed based upon theimplementation of the methodologies and systems provided herein.Examples of such other neuropsychiatric conditions may include, withoutlimitation: a risk that a subject may perform or repeat a criminal actand/or a harmful act, a risk of the subject having a psychiatric illness(such as schizophrenia), and a risk of a subject feigning a psychiatricillness.

Such a method for assessing such neuropsychiatric conditions may beoperating from one or more memory devices including computer-readableinstructions configured to instruct a computerized system to perform themethod, and the method may be operating on a computerized systemincluding one or more computer servers (or other available devices)accessible over a computer network such as the Internet or over someother data network. The method may include the following operations asshown in FIG. 7, which do not necessarily need to be performed in thestated order. Such operations may include an operation 80 of receivingbiomarker data associated from an analysis of the subject's biologicalsample. An operation 82 may involve receiving thought-marker dataobtained pertaining to one or more of the subject's recorded thoughts,spoken words, transcribed speech, and writings. An operation 84 mayinclude generating a biomarker score associated with theneuropsychiatric condition from the biomarker data. An operation 86 mayinclude generating a thought-marker score associated with theneuropsychiatric condition from the thought-marker data. An operation 88may involve calculating a neuropsychiatric condition score based, atleast in part, upon the biomarker score and the thought-marker score.

In an embodiment, the step of generating the biomarker score may includea step of assessing a level of at least a cytokine, a metabolite, apolymorphism, a genotype, a polypeptide, and an mRNA of the humansubject. For example, the step of generating the biomarker score mayinclude a step of assessing a level of a hydroxyindoleaceticacid(5HIAA).

In an embodiment, the step of generating a thought-marker score includesa step of determining a correlation between (a) the human subject'srecorded thoughts, spoken words, transcribed speech and/or writings; and(b) a corpus of thought data collected pertaining, at least in part, tothe neuropsychiatric condition. Further, this correlation may bedetermined, at least in part, utilizing natural language processingand/or machine learning algorithms.

In an embodiment, the method may further include a step of receivingclinical data of the subject associated with the neuropsychiatriccondition; may include a step of generating a clinical data score fromthe clinical data; and the step of calculating in neuropsychiatriccondition score may be based, at least in further part, upon theclinical data score. Further, the clinical data of the subjectassociated with the neuropsychiatric condition may include at least aportion of medical patient record data associated with the subject; mayinclude demographic data associated with the subject; and/or may includeinterview and/or survey data obtained from the subject. With thisembodiment, it is possible that the step of calculating aneuropsychiatric condition score may include steps of (a) normalizingthe biomarker score, (b) normalizing the thought-marker score, (c)normalizing the clinical data score and (d) calculating a mean of atleast the normalized biomarker, thought marker and clinical data scores.Further, the normalizing steps normalize between a numerical scale of0.0 to 1.0 and/or a scale of 0 and 10^(N), wherein N is an integer.Further, the step of generating a clinical data score may include a stepof calculating a percentage of risks associated with theneuropsychiatric condition from the subject compared to a predeterminedset of risks associated with the neuropsychiatric condition.

In an embodiment, the step of generating a biomarker score includes astep of calculating a composite score related to two or more biologicalmarkers associated with the neuropsychiatric condition from thebiomarker data.

In an embodiment, the step of calculating a neuropsychiatric conditionscore includes steps of (a) normalizing the biomarker score, (b)normalizing the thought marker score and (c) calculating a mean of atleast the normalized biomarker and the thought marker scores.

In an embodiment, the method further includes a step of automaticallyrecommending, based upon the calculated neuropsychiatric conditionscore, a subject's treatment regimen, a subject's counseling session, asubject's intervention program and/or a subject's care program.

Following from the above disclosure, it should be apparent to those ofordinary skill in the art that, while the methods and apparatuses hereindescribed constitute exemplary embodiments of the present invention, itis to be understood that the inventions contained herein are not limitedto the above precise embodiment and that changes may be made withoutdeparting from the scope of the invention. Likewise, it is to beunderstood that it is not necessary to meet any or all of the identifiedadvantages or objects of the invention disclosed herein in order to fallwithin the scope of the invention, since inherent and/or unforeseenadvantages of the present invention may exist even though they may nothave been explicitly discussed herein.

What is claimed is: 1-50. (canceled)
 51. A method for assessing aneuropsychiatric condition of a human subject, the method comprisingdetermining one or more neuropsychiatric condition associated biologicalmarkers in a biological sample from the subject to provide biomarkerdata for the subject, the markers determined by a method comprising oneor more of a polymerase chain reaction (PCR), a reverse transcriptionPCR reaction (RT-PCR), mass spectroscopy (MS), high pressure liquidchromatography (HPLC), LC-MS, DNA sequencing, and an enzyme-linked, beadbased, or sandwich immunoassay, generating, using one or more computerprocessors, a biomarker score based on the strength of the associationof the biomarker data with the neuropsychiatric condition, obtainingthought-marker data from the subject, the thought marker data includingone or more of the subject's recorded thoughts, spoken words,transcribed speech, and writings, generating, using one or more computerprocessors, a thought-marker score based on the strength of theassociation of the thought marker data with the neuropsychiatriccondition by a method comprising the steps of determining a correlationbetween (i) the thought marker data of the subject and (ii) a corpus ofthought data pertaining to the neuropsychiatric condition, thecorrelation determined using a machine learning method implementing aclassification algorithm selected from the group consisting of decisiontrees, classification rules, function models, and instance-based learnermethods, the machine learning method comprising extracting andquantifying relevant content features of the thought marker data andcreating a heterogeneous, multidimensional feature space, normalizingthe feature values, and generating the thought-marker score based uponthe strength of the correlation, and generating a neuropsychiatriccondition score based on the biomarker score and the thought-markerscore, thereby providing a quantitative assessment of theneuropsychiatric condition of the subject.
 52. The method of claim 51,wherein the neuropsychiatric condition is suicide attempt risk.
 53. Themethod of claim 52, wherein the step of determining the one or moreneuropsychiatric condition associated biological markers includes a stepof determining a level of a hydroxyindoleacetic acid (5HIAA).
 54. Themethod of claim 52, wherein the step of determining the one or moreneuropsychiatric condition associated biological markers includes a stepof determining the presence of the S and L alleles of the 5′ upstreamregulatory region of the serotonin transporter gene (5-HTTLPR).
 55. Themethod of claim 52, wherein the step of determining the one or moreneuropsychiatric condition associated biological markers includes a stepof determining the presence of one or more single nucleotidepolymorphisms taken from a group consisting of A218C of the TPH1 gene,A779C of the TPH1 gene, and A59G of the SLC6A3 gene.
 56. The method ofclaim 52, wherein the step of determining the one or moreneuropsychiatric condition associated biological markers includes a stepof determining an mRNA level of 5-HT(2A) mRNA.
 57. The method of claim52, wherein the step of determining the one or more neuropsychiatriccondition associated biological markers includes a step of determining alevel of one or more cytokines taken from a group consisting of IL-6,IL-2, IFN-γ, IL-4 and TGF-β1.
 58. The method of claim 52, wherein thestep of determining the one or more neuropsychiatric conditionassociated biological markers includes a step of determining a level ofserotonin (5-HT).
 59. The method of claim 51, further comprisingreceiving clinical data of the subject associated with theneuropsychiatric condition.
 60. The method of claim 59, wherein theclinical data includes one or more of data pertaining to a level ofinterpersonal discord, data pertaining to a presence of a mood disorder,data pertaining to a history of substance use, data pertaining to ahistory of impulsive aggression, data pertaining to a family history ofsuicidal behavior, data pertaining to access to weapons such asfirearms, and data pertaining to recent psychosocial stressors.
 61. Amethod for assessing a suicide attempt risk of a human subject, themethod comprising determining one or more suicide risk associatedbiological markers in a biological sample obtained from the subject by amethod comprising one or more of a polymerase chain reaction (PCR), areverse transcription PCR reaction (RT-PCR), mass spectroscopy (MS),high pressure liquid chromatography (HPLC), LC-MS, DNA sequencing, andan enzyme-linked, bead based, or sandwich immunoassay, receiving, usingone or more computer processors, one or more thought markers of thesubject, the one or more thought markers including one or more of thesubject's recorded thoughts, spoken words, transcribed speech, andwritings; executing, using one or more computer processors, a firstquery and transmitting the first query to a database to obtain aplurality of suicide notes associated with prior completions ofsuicides, the one or more processors being communicatively coupled tothe database using one or more communications networks; comparing, usinga machine learning method, the one or more thought markers and theobtained plurality of suicide notes to determine a correlation between(a) the one or more thought markers of the subject and (b) the obtainedplurality of suicide notes, and generating a thought-marker score basedupon a strength of the correlation, generating a suicide attempt riskscore based on a combination of the biomarker score and thethought-marker score; and generating, using the suicide attempt riskscore, an assessment of the subject.
 62. The method of claim 61, whereinthe machine learning method comprises an implementation of aclassification algorithm including at least one of a decision tree, aclassification rule, a function model, an instance-based learner method,and combinations thereof.
 63. The method of claim 61, wherein themachine learning method comprises extracting and quantifying relevantcontent features of the one or more thought markers and generating,based on the extracted and quantified content features, a heterogeneous,multidimensional feature space containing a plurality of feature valuescorresponding to the extracted and quantified content features.
 64. Themethod of claim 63, further comprising normalizing the generated featurevalues and generating, using the normalized generated feature values, athought-marker score based upon a strength of the correlation between(a) and (b).
 65. The method of claim 64, further comprising normalizing,using one or more computer processors, the one or more determinedsuicide risk associated biological markers and generating a normalizedscore for each marker based on the strength of marker's association withsuicide risk.
 66. The method of claim 65, further comprising generating,using one or more computer processors, a biomarker score based on a sumof individual normalized scores for the one or more determined suiciderisk associated biological markers.